Foreground detection: Combining background subspace learning with object smoothing model

Foreground detection is a challenging problem in complex scenes. In this paper, a novel foreground detection method is proposed which combines background subspace learning with object smoothing model. Considering background scenes in consecutive frames are almost the same, they are approximated using an efficient subspace learning technique which is based on 2D images. Due to the pixels of objects are usually clustered, an object smoothing model is adopted where a spatial smoothing constraint is imposed on its values during the estimation, and then it can be solved as a regularized matrix restoration problem with a spatial smoothing constraint. As a result, isolated noises can be suppressed while clustered foreground pixels can be preserved. We test our method on some challenging sequences and compare it with some other techniques. Experimental results show its effectiveness and robustness.